Collaborative Research: Modeling, Analysis, and Control of the Spatio-temporal Dynamics of Swarm Robotic Systems

协作研究：群体机器人系统时空动力学的建模、分析和控制

• 批准号: 1435709
• 负责人: Andrea Bertozzi
• 金额: $ 25万
• 依托单位: University of California-Los Angeles
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-09-01 至 2018-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1435709&HistoricalAwards=false
• 关键词: Collaborative; Research; Modeling; Analysis; Control

Massive populations, or swarms, of low-cost autonomous robots have the potential to collectively perform tasksover very large domains and time scales, succeeding even in the presence of failures, errors, and disturbances. It is becoming feasible to create robotic swarms in practice due to ongoing advances in computing, sensing, actuation, power, control, and 3D printing technologies. In recent years, the miniaturization of these technologies has led to many novel robot platforms for swarm applications, including micro aerial vehicles. However, it remains a challenge to reliably control arbitrary numbers of such resource-constrained robots in unknown environments where global information and communication are limited or undependable. This research project aims to overcome this challenge by developing a rigorous framework for the scalable control of robotic swarms in realistic environments. The framework combines techniques from the fields of fluid dynamics, signal reconstruction, control theory, and optimization. This work provides a theoretically grounded approach for automatically programming robotic swarms to perform a diverse set of tasks of wide benefit to society, including environmental monitoring and exploration, disaster recovery, security operations, and even biomedical imaging and targeted cancer therapies at the nanoscale. This project develops a formal methodology for analyzing and controlling the spatiotemporal dynamics of robotic swarms that are to be deployed in complex unknown environments. The designed robot control policies incorporate stochastic behaviors such as random encounters with environmental features and produce target collective behaviors within a specified degree of confidence. The confidence estimates are computed using a novel application of vortex methods, originally derived for fluid dynamic models and recently adapted to obtain continuum limits of discrete swarm models that incorporate pairwise interaction rules for maintenance of group structure. The control approach uses new computational algorithms for compressive sensing to reconstruct scalar environmental fields from sparse robot sensor data and to design efficient strategies for robot data collection. The methodology is demonstrated with a case study on designing control policies for micro aerial vehicles that are tasked to pollinate a crop field. Both computer models and testbed field experiments are used to validate theoretical predictions for the confidence estimates on system performance. Beyond robotics, the project provides analytical tools for a deeper understanding of the complex macroscopic behaviors of systems that can be represented with similar models, including non-well-mixed chemical reaction networks and natural swarms such as social insect colonies.

低成本自主机器人的大量人群或群体有可能共同执行TaskSover非常大的域和时间尺度，即使在存在故障，错误和干扰的情况下也成功。 由于计算，感应，驱动，功率，控制和3D打印技术的持续进展，在实践中创建机器人群是可行的。 近年来，这些技术的小型化导致了许多新型的机器人平台，用于包括微型航空车。 但是，在全球信息和通信是有限或不可依赖的未知环境中可靠地控制任意数量的这种资源受限机器人的任意数量仍然是一个挑战。 该研究项目旨在通过开发严格的框架来克服这一挑战，以在现实环境中为机器人群的可扩展控制。 该框架结合了流体动力学，信号重建，控制理论和优化领域的技术。 这项工作为自动编程机器人群进行了一种理论上的方法，以对社会执行各种各样的任务，包括环境监测和探索，灾难恢复，安全操作，甚至生物医学成像以及纳米级的癌症疗法。 该项目开发了一种形式的方法，用于分析和控制应在复杂的未知环境中部署的机器人群的时空动力学。设计的机器人控制策略结合了随机行为，例如具有环境特征的随机遇到，并在指定的置信度范围内产生目标集体行为。置信度估计是使用最初用于流体动态模型的新型涡流方法的新应用来计算的，最近适应了离散群模型的连续限制，这些群体模型结合了配对相互作用规则以维持组结构。控制方法使用新的计算算法进行压缩传感，以从稀疏的机器人传感器数据中重建标量环境领域，并为机器人数据收集设计有效的策略。 该方法通过一项案例研究来证明，该案例研究针对负责授粉农作物领域的微型航空车的控制政策。 计算机模型和测试床现场实验均用于验证有关系统性能的置信度估计的理论预测。除了机器人技术之外，该项目还提供了分析工具，以更深入地了解系统的复杂宏观行为，这些行为可以用相似的模型来表示，包括非玻璃混合化学反应网络和自然群（例如社交昆虫菌落）。